1. Technological Field
This disclosure pertains generally to image processing, and more particularly to image processing utilizing a phase transform with an output phase image for edge detection.
2. Background Discussion
Exponential growth in the amount of digital data generated by sensors and computers has resulted in a technological problem called “Big Data” bottleneck. One of the most problematic issues when working with Big Data is to analyze and make sense out of the huge amount of the flooding data. In past decades, many computer vision methods, such as edge detection, object recognition and machine learning algorithms have been developed for Big Data handling.
Edge detection is the name for a set of mathematical methods for identifying patterns in a digital image where brightness or color changes abruptly. Applying an edge detection process to an image is the basis for numerous forms of object detection, shape, classification, movement detection, and so forth. Edge detection also reduces the digital file size while preserving important information, albeit data compression is not the main objective in edge detection.
There are many methods for edge detection, but most of them can be grouped into two categories, search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative, and then searching for local directional maxima of the gradient magnitude. The zero-crossing based methods search for zero crossings in a second-order derivative computed from the image.
Sobel operator is one of the earliest advanced methods developed for edge detection. It is a discrete differentiation operator performed at each point in the image, the result of the Sobel operator is either the corresponding gradient vector or the norm of this vector. The gradient approximation that is produced is relatively crude, in particular for high frequency variations in the image.
Other edge detection methods, such as Canny, Prewitt, Roberts, Log and Zero cross exist for computer vision applications. The Canny edge detector, considered as state-of-the-art, uses a multi-stage algorithm to detect edges in an image. Canny uses the calculus of variations toward optimizing a given function. The optimal function is described by the sum of four exponential terms, however, it can be approximated by the first derivative of a Gaussian.
However, even the most advanced Canny edge detection approach suffers from a number of shortcomings that limit its ability for discerning edges and objects under the best conditions, and whose results degrade significantly under adverse image situations and conditions.
Accordingly, a need exists for new edge detection apparatus and methods which provide enhanced edge detection abilities which can be utilized in wide range of conditions. The present disclosure fulfill those needs and others, while overcoming other shortcomings of existing methods.